System and method for analyzing features in product reviews and displaying the results

ABSTRACT

A system and method for analyzing reviews is disclosed herein. User-generated content (UGC) such as on-line reviews of products can be broken up in to different features of the products being reviewed. The UGC is analyzed to find each mention of each feature. Then a tag cloud or other visual indicia of the features is created. The tag cloud or other visual indicia displays a certain subset of the features, with an indication of how often certain features are discussed. The indication of how often features are discussed can be the font size of the tag cloud or other visual indicia. Other embodiments are also disclosed.

TECHNICAL FIELD

This disclosure relates generally to reviews, and relates moreparticularly to a system and method for more thoroughly analyzingreviews.

BACKGROUND

There are many sites available on the Internet that allow a user toaccess reviews of products. However, these reviews can sometimes bemisleading, especially if the reviews rely on an overall score of aproduct. Because some reviews might be negative for reasons that are notrelevant to all users, it would be desirable to have a method and systemfor analyzing reviews based on features of a product.

BRIEF DESCRIPTION OF THE DRAWINGS

To facilitate further description of the embodiments, the followingdrawings are provided in which:

FIG. 1 illustrates a front elevation view of a computer system that issuitable for implementing an embodiment of the system disclosed in FIG.3;

FIG. 2 illustrates a representative block diagram of an example of theelements included in the circuit boards inside a chassis of the computersystem of FIG. 1;

FIG. 3 illustrates a flowchart for an exemplary method of calculating afeature score, according to an embodiment;

FIG. 4 illustrates a table of reviews and features, according to anembodiment;

FIG. 5 illustrates a table of reviews and features, according to anembodiment;

FIG. 6 illustrates a table of reviews and features, according to anembodiment;

FIG. 7 is an exemplary screen shot illustrating the presentation offeature scores;

FIG. 8 is a block diagram of a system arranged to perform various tasksaccording to an embodiment;

FIG. 9 is a flowchart illustrating a method for annotating reviews;

FIG. 10 illustrates an exemplary screen shot according to an embodiment;

FIG. 11 is an exemplary screen shot according to an embodiment;

FIG. 12 is an exemplary screen shot according to an embodiment;

FIG. 13 is a flowchart illustrating a method for annotating reviews;

FIG. 14 is an exemplary screen shot according to an embodiment;

FIG. 15 is a flowchart illustrating a method for creating a tag cloud;and

FIG. 16 is a block diagram of a system arranged to perform various tasksaccording to an embodiment.

For simplicity and clarity of illustration, the drawing figuresillustrate the general manner of construction, and descriptions anddetails of well-known features and techniques might be omitted to avoidunnecessarily obscuring the present disclosure. Additionally, elementsin the drawing figures are not necessarily drawn to scale. For example,the dimensions of some of the elements in the figures might beexaggerated relative to other elements to help improve understanding ofembodiments of the present disclosure. The same reference numerals indifferent figures denote the same elements.

The terms “first,” “second,” “third,” “fourth,” and the like in thedescription and in the claims, if any, are used for distinguishingbetween similar elements and not necessarily for describing a particularsequential or chronological order. It is to be understood that the termsso used are interchangeable under appropriate circumstances such thatthe embodiments described herein are, for example, capable of operationin sequences other than those illustrated or otherwise described herein.Furthermore, the terms “include,” and “have,” and any variationsthereof; are intended to cover a non-exclusive inclusion, such that aprocess, method, system, article, device, or apparatus that comprises alist of elements is not necessarily limited to those elements, but mightinclude other elements not expressly listed or inherent to such process,method, system, article, device, or apparatus.

The terms “left,” “right,” “front,” “back,” “top,” “bottom,” “over,”“under,” and the like in the description and in the claims, if any, areused for descriptive purposes and not necessarily for describingpermanent relative positions. It is to be understood that the terms soused are interchangeable under appropriate circumstances such that theembodiments of the apparatus, methods, and/or articles of manufacturedescribed herein are, for example, capable of operation in otherorientations than those illustrated or otherwise described herein.

The terms “couple,” “coupled,” “couples,” “coupling,” and the likeshould be broadly understood and refer to connecting two or moreelements mechanically and/or otherwise. Two or more electrical elementsmight be electrically coupled together, but not be mechanically orotherwise coupled together. Coupling might be for any length of time,e.g., permanent or semi-permanent or only for an instant. “Electricalcoupling” and the like should be broadly understood and includeelectrical coupling of all types. The absence of the word “removably,”“removable,” and the like near the word “coupled,” and the like does notmean that the coupling, etc. in question is or is not removable.

As defined herein, two or more elements are “integral” if they arecomprised of the same piece of material. As defined herein, two or moreelements are “non-integral” if each is comprised of a different piece ofmaterial.

As defined herein, “approximately” can, in some embodiments, mean withinplus or minus ten percent of the stated value. In other embodiments,“approximately” can mean within plus or minus five percent of the statedvalue. In further embodiments, “approximately” can mean within plus orminus three percent of the stated value. In yet other embodiments,“approximately” can mean within plus or minus one percent of the statedvalue.

DESCRIPTION OF EXAMPLES OF EMBODIMENTS

In one embodiment, a method can comprise: producing a feature set bycreating a list of features for a product; analyzing user-generatedcontent to find one or more mentions of each feature of the feature set;analyzing each of the one or more mentions of each feature of thefeature set to determine a total polarity of each of the one or morementions; calculating a feature score using the total polarity of eachfeature of the feature set; and displaying the feature score of eachfeature to a user.

In one embodiment, a system can comprise: one or more processingmodules; and one or more non-transitory storage modules storingcomputing instructions configured to run on the one or more processingmodules and perform the acts of producing a feature set by creating alist of a features for a product; analyzing user-generated content tofind one or more mentions of each feature of the feature set; analyzingeach of the one or more mentions of each feature of the feature set todetermine a total polarity of each of the one or more mentions;calculating a feature score using the total polarity of each feature ofthe feature set; and displaying the feature score of each feature to auser.

In one embodiment, a method can comprise: analyzing one or more reviewsin a set of reviews to determine a set of features; matching the set offeatures with the set of reviews to create an annotated set of reviews,wherein each review is matched with one or more annotations; for eachannotation in each review in the annotated set of reviews, marking theannotation as positive for a feature if a feature score is greater thana first predetermined score; for each annotation in each review in theannotated set of reviews, marking the annotation as negative for afeature if a feature score is less than a second predetermined score;and transmitting for display one or more reviews of the annotated set ofreviews; wherein: each of the features of the set of features representsa different aspect of a product being reviewed; and the feature score ofa feature is calculated by determining a total polarity of reviews.

In one embodiment, a system can comprise: one or more processingmodules; and one or more non-transitory storage modules storingcomputing instructions configured to run on the one or more processingmodules and perform the acts of analyzing one or more reviews in a setof reviews to determine a set of features; matching the set of featureswith the set of reviews to create an annotated set of reviews, whereineach review is matched with one or more annotations; for each annotationin each review in the annotated set of reviews, marking the annotationas positive for a feature if a feature score is greater than a firstpredetermined score; for each annotation in each review in the annotatedset of reviews, marking the annotation as negative for a feature if afeature score is less than a second predetermined score; andtransmitting for display one or more reviews of the annotated set ofreviews; wherein: each of the features of the set of features representsa different aspect of a product being reviewed; and the feature score ofa feature is calculated by determining a total polarity of reviews.

In one embodiment a method can comprise: analyzing one or more reviewsin a set of reviews to determine a set of features analyzed in the setof reviews; for each review in the set of reviews, noting each featureof the set of features analyzed in the review; receiving a request froma user indicating a desire to view a selected feature in the set offeatures; determining which reviews in the set of reviews are noted withthe selected feature and placing those reviews in a subset of reviews;and transmitting for display a summary of the subset of reviews.

In one embodiment, a system can comprise: a user input device; a displaydevice; one or more processing modules; and one or more non-transitorystorage modules storing computing instructions configured to run on theone or more processing modules and perform the acts of: analyzing one ormore reviews in a set of reviews to determine a set of features analyzedin the set of reviews; for each review in the set of reviews, notingeach feature of the set of features analyzed in the review; receiving arequest from a user indicating a desire to view a selected feature inthe set of features; determining which reviews in the set of reviews arenoted with the selected feature and placing those reviews in a subset ofreviews; and transmitting for display a summary of the subset of reviews

Turning to the drawings, FIG. 1 illustrates an exemplary embodiment of acomputer system 100, all of which or a portion of which can be suitablefor implementing the techniques described herein. As an example, adifferent or separate one of a chassis 102 (and its internal components)can be suitable for implementing the techniques described herein.Furthermore, one or more elements of computer system 100 (e.g., arefreshing monitor 106, a keyboard 104, and/or a mouse 110, etc.) canalso be appropriate for implementing the techniques described herein.Computer system 100 comprises chassis 102 containing one or more circuitboards (not shown), a Universal Serial Bus (USB) port 112, a CompactDisc Read-Only Memory (CD-ROM), Digital Video Disc (DVD) drive, orBlu-Ray drive 116, and a hard drive 114. A representative block diagramof the elements included on the circuit boards inside chassis 102 isshown in FIG. 2. A central processing unit (CPU) 210 in FIG. 2 iscoupled to a system bus 214 in FIG. 2. In various embodiments, thearchitecture of CPU 210 can be compliant with any of a variety ofcommercially distributed architecture families.

Continuing with FIG. 2, system bus 214 also is coupled to a memorystorage unit 208, where memory storage unit 208 comprises both read onlymemory (ROM) and random access memory (RAM). Non-volatile portions ofmemory storage unit 208 or the ROM can be encoded with a boot codesequence suitable for restoring computer system 100 (FIG. 1) to afunctional state after a system reset. In addition, memory storage unit208 can comprise microcode such as a Basic Input-Output System (BIOS) orUnified Extensible Firmware Interface (UEFI). In some examples, the oneor more memory storage units of the various embodiments disclosed hereincan comprise memory storage unit 208, a USB-equipped electronic device,such as, an external memory storage unit (not shown) coupled touniversal serial bus (USB) port 112 (FIGS. 1-2), hard drive 114 (FIGS.1-2), and/or CD-ROM, DVD drive, or Blu-Ray drive 116 (FIGS. 1-2). In thesame or different examples, the one or more memory storage units of thevarious embodiments disclosed herein can comprise an operating system,which can be a software program that manages the hardware and softwareresources of a computer and/or a computer network. The operating systemcan perform basic tasks such as, for example, controlling and allocatingmemory, prioritizing the processing of instructions, controlling inputand output devices, facilitating networking, and managing files. Someexamples of common operating systems can comprise variousversions/distributions of Microsoft® Windows® operating system (OS),Apple® OS X, UNIX® OS, and Linux® OS.

As used herein, “processor” and/or “processing module” means any type ofcomputational circuit, such as but not limited to a microprocessor, amicrocontroller, a controller, a complex instruction set computing(CISC) microprocessor, a reduced instruction set computing (RISC)microprocessor, a very long instruction word (VLIW) microprocessor, agraphics processor, a digital signal processor, or any other type ofprocessor or processing circuit capable of performing the desiredfunctions. In some examples, the one or more processors of the variousembodiments disclosed herein can comprise CPU 210.

In the depicted embodiment of FIG. 2, various I/O devices such as a diskcontroller 204, a graphics adapter 224, a video controller 202, akeyboard adapter 226, a mouse adapter 206, a network adapter 220, andother I/O devices 222 can be coupled to system bus 214. Keyboard adapter226 and mouse adapter 206 are coupled to keyboard 104 (FIGS. 1-2) andmouse 110 (FIGS. 1-2), respectively, of computer system 100 (FIG. 1).While graphics adapter 224 and video controller 202 are indicated asdistinct units in FIG. 2, video controller 202 can be integrated intographics adapter 224, or vice versa in other embodiments. Videocontroller 202 is suitable for refreshing monitor 106 (FIGS. 1-2) todisplay images on a screen 108 (FIG. 1) of computer system 100 (FIG. 1).Disk controller 204 can control hard drive 114 (FIGS. 1-2), USB port 112(FIGS. 1-2), and CD-ROM drive 116 (FIGS. 1-2). In other embodiments,distinct units can be used to control each of these devices separately.

In some embodiments, network adapter 220 can comprise and/or beimplemented as a WNIC (wireless network interface controller) card (notshown) plugged or coupled to an expansion port (not shown) in computersystem 100 (FIG. 1). In other embodiments, the WNIC card can be awireless network card built into computer system 100 (FIG. 1). Awireless network adapter can be built into computer system 100 by havingwireless communication capabilities integrated into the motherboardchipset (not shown), or implemented via one or more dedicated wirelesscommunication chips (not shown), connected through a PCI (peripheralcomponent interconnector) or a PCI express bus of computer system 100(FIG. 1) or USB port 112 (FIG. 1). In other embodiments, network adapter220 can comprise and/or be implemented as a wired network interfacecontroller card (not shown).

Although many other components of computer system 100 (FIG. 1) are notshown, such components and their interconnection are well known to thoseof ordinary skill in the art. Accordingly, further details concerningthe construction and composition of computer system 100 and the circuitboards inside chassis 102 (FIG. 1) are not discussed herein.

When computer system 100 in FIG. 1 is running, program instructionsstored on a USB-equipped electronic device connected to USB port 112, ona CD-ROM or DVD in CD-ROM and/or DVD drive 116, on hard drive 114, or inmemory storage unit 208 (FIG. 2) are executed by CPU 210 (FIG. 2). Aportion of the program instructions, stored on these devices, can besuitable for carrying out at least part of the techniques describedherein.

Although computer system 100 is illustrated as a desktop computer inFIG. 1, there can be examples where computer system 100 can take adifferent form factor while still having functional elements similar tothose described for computer system 100. In some embodiments, computersystem 100 can comprise a single computer, a single server, or a clusteror collection of computers or servers, or a cloud of computers orservers. Typically, a cluster or collection of servers can be used whenthe demand on computer system 100 exceeds the reasonable capability of asingle server or computer. In certain embodiments, computer system 100can comprise a portable computer, such as a laptop computer. In certainother embodiments, computer system 100 can comprise a mobile device,such as a smart phone or a tablet. In certain additional embodiments,computer system 100 can comprise an embedded system. It should also beunderstood that a particular configuration of computer system 100 mightor might not contain each of the items shown in FIG. 1 or 2 or mightcontain multiple of each of the items shown in FIG. 1 or 2. For example,certain implementations of computer system 100 might not contain aCD-ROM, DVD, or Blu-Ray drive 116. Other implementations of computersystem 100 might contain two CD-ROM, DVD, or Blu-Ray drives 116. Otherimplementations of computer system 100 can contain 2 or more monitors106. Other implementations of computer system 100 could contain nomonitors. Other implementations of computer system 100 can containequivalents to certain items. For example, hard drive 114 can bereplaced or augmented by a solid-state drive (SSD).

The Internet gives users the ability to research any product or servicethat the user wishes to purchase to a much greater extent than possiblebefore the Internet. For example, users can go to an eCommerce website,search for a product or service, and analyze the opinions of manydifferent people regarding the product or service. This ability can beparticularly useful for users who are comparing two competing productsor services.

One problem with existing reviews is that user generated content (“UGC”)(including, but not limited to, reviews at eCommerce sites, blogentries, on-line articles, and the like) often includes a rating system(such as a star system featuring from 1 to 5 stars to rate a product),but the ratings might not accurately reflect a user's actual views of aproduct. For example, a user might give a low rating for a product, butthe low rating is solely due to a slower delivery than expected (thusbecoming a review of the eCommerce vendor as opposed to the product).Another review might give a low rating on a product for reasons thatmight not be important to other users. For example, one review of asmartphone could give a low rating for a particular smartphone primarilybecause of poor camera capabilities. But another user might not careabout camera capabilities because she is not interested in photos.

Another example might involve a more thorough review that analyzesseveral different aspects of a product and assigns an overall rating ofthe product. However, some users might be particularly interested insome aspects of the review, but not other aspects of the review. Forexample, some users might not care how comfortable the back seat of acar is because they do not plan to use the back seat on a regular basis.So a review of a car that is otherwise positive, but gives an overallneutral or negative review to a car because of a small back seat, mightnot be relevant to some users.

While it can be useful to analyze reviews and determine an overallsentiment of a review, it can be more insightful to determine a specificsentiment of each aspect of a review, to provide a user with moreinformation when making a decision.

As of today, if a user wants to compare two similar products, his/heronly choices is either to make use of the overall scores provided byeCommerce sites or by reading through each review and noting what eachreviewer has written. It should be clear that just making use of theoverall score might not be a great deal of help because a single overallscore might not give a detailed explanation as to why one product isbetter than or worse than another. On the other hand, reading througheach review can become a very time consuming activity (e.g., on someeCommerce sites, popular products can have tens of thousands ofreviews).

In actual usage, more than the overall rating of a product, users areinterested in individual aspects of products. For example, whencomparing two cameras, a user might want to compare not only the overallrating of each product, but also individual features of each product,such as body, lens, focal lengths, zoom, and the like.

Sentiment analysis can be used to provide an automated method to analyzereviews. However, sentiment analysis is mostly limited to the overallsentiment projected by the entire text. Therefore, for any product,there is no existing automated system that groups features of theproduct with the sentiment expressed specifically with regard to thatfeature. The representations that come closest to this ideal are thereview systems of many e-commerce sites that provide overall ratings fora product based on entire reviews given by the user. These overallratings usually range from one to five stars for the product, and theseoverall ratings are explicitly added by the reviewer. Typically, thereis no way to generate an overall rating from the text of a review. Forthe above and other reasons, overall ratings can present a flawedinsight to the overall likeability of a product.

One solution is to provide the user with not only an overall rating forthe product, but also the underlying features along with a correspondingscore for each feature. Unlike the existing systems mentioned above, anembodiment can apply sentiment analysis to the text to arrive at a scorefor each of its features. Some embodiments have the capability toconcentrate on only the relevant content from a review for every suchfeature and derive a rating for it. These embodiments provide a betterrepresentation and a deeper insight to the product from the dataavailable. With the addition of a feature score, an embodiment can bemore informative than an overall one-star to five-star rating systemthat is prevalent in today's rating systems.

Turning ahead in the drawings, FIG. 3 is a flowchart illustrating amethod 300 for calculating a feature score. The search being performedcan be performed on a computer system 100 (FIG. 1). Method 300 is merelyexemplary and is not limited to the embodiments presented herein. Method300 can be employed in many different embodiments or examples notspecifically depicted or described herein. In some embodiments, theprocedures, the processes and/or the activities of method 300 can beperformed in the order presented. In other embodiments, the procedures,the processes, and/or the activities of method 300 can be performed inany other suitable order. In still other embodiments, one or more of theprocedures, the processes, and/or the activities of method 300 can becombined or skipped.

For each product being analyzed, a feature set for the product isproduced (block 302). The feature set will be the set of attributes forwhich the analysis will be done. There can be several manners in which afeature set can be created. In some embodiments, the specifications canbe analyzed, and the most important attributes mentioned in itsspecifications can be added to the feature set. In some embodiments, thefeature set of a competing product can be analyzed to produce a featureset for the product being analyzed. In some embodiments, other methodscan be used such as, for example, a single scan of all the reviews canbe executed to obtain a list of relevant features on the basis ofterm-frequency.

From this initial feature set, synonyms and related words for eachfeature can be added to the feature set. It should be understood thatthis technique is not limited to synonyms. For example, some reviewsmight refer to the “weight” of a product while others might refer to the“mass” of a product. Related words can be more encompassing than justsynonyms. Referring to the same example of weight/mass, some reviewsmight refer to a product as “light,” and other reviews might refer tothe same product as “heavy,” sometimes without even using the terms“weight” or “mass.” The use of modifiers also can be taken into account.For example, the phrases “not heavy” and “very heavy” should be analyzedvery differently. Terms such as the above can be added to the featureset as terms to be analyzed.

The result is a feature set: f₁, f₂, . . . , f_(m)

An exemplary feature set for a camera might be (body, lens, zoom, flash,screen battery, focus, weight, face-detection, pixels, etc.)

Thereafter, for every feature in the feature set, the user-generatedcontent (“UGC”) is analyzed to find mentions of each feature (block304). Thereafter, each of the mentions can be analyzed to determinepolarity of each mention (block 306). Any known semantic analysis toolcan be used to perform this analysis. In one embodiment, Alchemy API canbe used to perform this analysis. Normalizing the total polarity interms of number of occurrences provides the feature score for thefeature in that review. Additionally, the number of occurrences can bean indication of the importance of the feature in the review.

The following is an example of how the polarity can be determined.

There is a set of reviews: R₁, R₂, R₃, . . . , R_(n)

For a feature f_(i) in the feature set, the number of occurrences inreview R_(j) is n_(ij).

For every mention k of feature f_(i) in review R_(j), we get a polarityof the sentiment expressed in that occurrence=s_(k).

The total polarity of the feature f_(i) in review R_(j) is given by thefollowing formula:Total polarity (f _(i) ,R _(j))=Σ_(k=1) ^(n) ^(ij) s _(k)

Example: Here is an exemplary camera review R₁:

“Many people write reviews right after they get a product so they don'talways note when something goes wrong down the line. I have had my Canon7D now for well over a year and can happily report that it is by far thebest camera I have ever owned (and I have had many)! It has never had asingle problem, the battery seems to last forever (seriously, I use itdaily and I think I charge it once every month or two, after thousandsof shots), and the images are spectacular. I have a number of Canonlenses including the 24-105L/f4 that I use most as well as a 50 mm/f1.4and the incredible 100 mm macro lens from Canon. I also have a wideangle lens that I use hardly ever. I am a semi-pro and have beenshooting since high school (about 25 years now). I photograph naturemost often (as well as my immediate family on a daily basis, including asquirmy toddler). It works very well as a point and shoot as well so Ican hand it to my daughter on all automatic settings and she gets superbphotographs out of it every time. I shoot mostly on A-priority ormanual. The controls are very intuitive (my 12-year old has no problemsfiguring everything out). I download my photos into Lightroom andcatalog/edit there. It is very easy to manage my thousands of photosthis way. I cannot recommend this camera or the Canon L lenses highlyenough! Worth every penny.”

For the feature f₁, lens, the sentences that will be considered are theones that are underlined above. The number of occurrences in review R₁for feature f₁ is n_(ij)=3. A sentiment analysis for each of thesesentences is performed. The polarity can be recorded as s₁, s₂, and s₃.A sentiment analysis system can assign each of these polarities between−1 and 1, depending on the content of the sentence. The scale used forpolarities, however, can range between any two numbers. Thus, in someembodiments, the polarity can be between −1 and 1. In other embodiments,the polarity can be between 0 and 10. In other embodiments, the polaritycan be between 0 and 100. An embodiment can readily convert from onepolarity scale to a different polarity scale through the use ofaddition, subtraction, multiplication, division, and the like.

With reference to FIG. 4, a table 400 is presented that illustrates anexemplary case with multiple reviews and features. Each of columns 410,420, and 430 represent different reviews. Each of rows 415, 425, 435,and 445 represents different features of the product being reviewed. Theintersection of each row and column represents each feature in eachreview. At the intersection of each row and column is one or morepolarities s_(i). There can be multiple polarities representing eachmention of a particular feature in a particular review. For example, inthe review presented above, there can be three different polarities, onefor each sentence that mentions a lens.

The table presented in FIG. 4 is merely exemplary. In actual use, therewill likely be many more than three reviews and more than four features.In addition, a similar table can exist for each product in a particulardatabase. Some databases contain thousands or even millions of products(for example, the databases of large eCommerce providers).

Returning to FIG. 3, the scores over all occurrences of a feature arenormalized to obtain feature score Su (block 308).

An exemplary formula to calculate a feature score is as follows:

${{feature}\mspace{14mu}{score}\mspace{14mu}\left( {f_{i},R_{j}} \right)} = {S_{ij} = \frac{\left( {\sum\limits_{k = 1}^{n_{ij}}s_{k}} \right)}{n_{ij}}}$

Hence, for every feature/review pair, a feature score S_(ij) is obtainedsuch that (−1)<=S_(ij)<=1. Therefore, after normalization, the table ofFIG. 5 is the result.

With reference to FIG. 5, a table 500 is presented that illustrates anexemplary case with multiple reviews and features. The table presentedin FIG. 5 is merely exemplary. In actual use, there will likely be manymore than three reviews and many more than four features. In addition, asimilar table can exist for each product in a particular database. Somedatabases contain thousands or even millions of products (for example,the databases of large eCommerce providers).

Each of columns 510, 520, and 530 represent different reviews. Each ofrows 515, 525, 535, and 545 represents different features of the productbeing reviewed. The intersection of each row and column represents eachfeature in each review. The feature score Su is calculated in a mannersuch as that presented above.

It can be illustrative to compare FIG. 4 and FIG. 5. Here, it can beassumed that FIG. 5 is a different representation of the data from FIG.4. For example, with reference to FIG. 4, feature 1 of review 1 hadthree different polarities. With reference to FIG. 5, the threedifferent polarities of feature 1 of review 1 have been combined to forma feature score for that review.

An embodiment can access table 500, aggregate all the scores for afeature, and normalize the scores by the number of reviews to obtain thefinal feature score. Returning to FIG. 3, the aggregation of scores canbe represented by block 310. Thereafter, the feature score can bedisplayed to a user (block 312).

After a user has read all the reviews he desires to make a decision, anembodiment can be configured to receive an order that purchases aproduct. Thereafter, an embodiment can be configured to facilitate thedelivery of the product to the user. Such a facilitation can beaccomplished in one of a variety of ways known in the art or developedin the future. In some embodiments, the information from the order canbe sent to a fulfillment center which will perform delivery services. Inother embodiments, the information can be sent to a brick and mortarstore, which can place the product in a special area for pick-up at alater time by the user.

In other words, each of the feature scores can be averaged across everyreview or across every mention in every review. An exemplary result ofthis technique is shown as table 600 in FIG. 6. In table 600, column 610is the only column and represents the aggregate feature score. Rows 615,625, 635, and 645 are each of the features.

The table presented in FIG. 6 is merely exemplary. In actual use, therecan be more than four features. In addition, a similar table can existfor each product in a particular database. Some databases containthousands or even millions of products (for example, the databases oflarge eCommerce providers).

After calculating the aggregated feature scores across all the reviews,it can be desirable to display the feature scores in a manner that wouldbe useful to the users. In one embodiment, there is a new layout toshowcase different aspects/features of an entity. One use case of anembodiment can be in eCommerce web sites. However, the design discussedin the following paragraphs can be generalized and applied to othertypes of usage also.

After the above embodiment is performed, an embodiment now has a scoreof every feature for each review. If the score s_(ij) is above athreshold (e.g., zero), then the feature f_(i) can be determined to be a“good” feature based on the review R_(j). Similarly, if the score isbelow a threshold (e.g., 0), then the feature f_(i) can be determined tobe a “bad” feature based on the review R_(j). The threshold between a“good” feature and a “bad” feature can be different from that describedabove. In an embodiment where the threshold is 0 for both good featuresand bad features, the feature score can be only good or be bad (exceptfor the unusual case of a feature score being exactly 0). In someembodiments, the threshold scores can be set such that there are somefeatures that are neither good nor bad. For example, the threshold forbeing considered a “good” feature can be above 0.5. The threshold forbeing considered a “bad” feature can be negative 0.5. Thus, there wouldbe some features that are good, some features that are bad, and somefeatures that are neutral (i.e., neither “good” nor “bad”). In someembodiments, the threshold for being considered “good” or “bad” can beadjusted by a user.

In some embodiments, as part of block 312, all feature scores can bedisplayed to a user. In some embodiments, only “good” feature scores canbe displayed to a user. In some embodiments, only “bad” feature scorescan be displayed to a user. In some embodiments, both “good” and “bad”feature scores can be displayed to a user. Such a task can beaccomplished in a number of different manners. In some embodiments, afirst threshold score is established. The feature score for each featureof a product is compared to the first threshold score. Thereafter, onlyfeature scores greater than the first threshold score are displayed. Ina similar manner, a second threshold score can be established.Thereafter, only feature scores lower than the second threshold scoreare displayed.

Establishing the first or second threshold score can occur in a numberof different manners. In some embodiments, the first or second thresholdscore can be pre-determined. In some embodiments, the first or secondthreshold scores can be set by a user. In some embodiments, setting athreshold score can be accomplished by using a drop down box on a webpage. In some embodiments, setting a threshold score can be accomplishedby using a filter. Other methods of setting a threshold score can beused.

As described above, there can be several negatives to the current trendof using a single score to represent a user's opinion of a product. Insome embodiments, after calculating the various scores for each feature,it can be desirable to display the scores to a user in a manner to allowa user to more accurately evaluate a product.

Moving ahead to FIG. 7, an exemplary screen shot 700 illustrating thepresentation of feature scores is presented. FIG. 7 is merely exemplaryand embodiments of the screen representation and menu system are notlimited to the embodiments presented herein. The screen representationand menu system can be employed in many different embodiments orexamples not specifically depicted or described herein. As an example,screen shot 700 can be shown on a screen of refreshing monitor 106 (FIG.1).

Returning to FIG. 7, at the top of exemplary screen shot 700 is name 702of the product for which the review scores are applicable. It should beunderstood that while only the name of the product is shown in screenshot 700, additional information might also be present in variousembodiments. For example, a photo of the product, a brief description ofthe product, the manufacturer of the product, and other relevantinformation might be listed. It should also be understood that, whilethe example here pertains to a camera and various features of a camera,embodiments can be used on products of any type. In addition,embodiments are not limited to products, as embodiments can be used torate services (such as restaurants, auto repair shops, hair salons, andthe like) as well.

Underneath name 702 is a list of various features. In the examplepresented in FIG. 7, features include features 710, 720, 730, 740, 750,and 760. Each of the features is listed next to a feature score, such asscores 714, 724, 734, 744, 754, and 764. Scores 714, 724, 734, 744, 754,and 764 are shown in numerical form in FIG. 7. Some embodiments mightalso include a graphical representation of the feature score. In FIG. 7,such graphical representations can include bar graphs 717, 727, 737,747, 757, and 767. It should be understood that the graphicalrepresentation is not limited to a bar graph and can take a variety ofdifferent formats.

Screen shot 700 can be displayed to a user at various times and invarious manners. For example, a user might enter an eCommerce site andtype in a search term for a camera. The result could include the layoutof FIG. 7 for multiple cameras at once. In such a manner, a user caneasily compare different features among different cameras. For example,in FIG. 7, flash has a relatively low score of 37. So some users mightview that negatively and decide to purchase a different camera with ahigher score for flash. But a different set of users might not beinterested in flash performance and might be intrigued by the relativelyhigh body score of 86.

There are other aspects that a user can adjust. For example, in someembodiments, the feature scores can be hidden, except the overall score.The result would be similar to many eCommerce sites today, with only anoverall score being presented. However, with the selection of an option(such as a drop box, check box, radio button, and the like), additionalfeature scores can be presented to the user.

Such a layout can be beneficial in improving customer satisfaction. Asopposed to a large amount of clutter that is currently present over mostcustomer review portals, an embodiment can serve as a one-stop solution.Such a solution can help in retaining and attracting new customers.

Turning ahead in the figures, FIG. 8 illustrates a block diagram of asystem 800 arranged to perform various tasks. System 800 is merelyexemplary and is not limited to the embodiments presented herein. System800 can be employed in many different embodiments or examples notspecifically depicted or described herein. In some embodiments, certainelements or modules of system 800 can perform various procedures,processes, and/or acts. In other embodiments, the procedures, processes,and/or acts can be performed by other suitable elements or modules. Asan example, system 800 can be implemented using computer system 100(FIG. 1).

Returning to FIG. 8, in a number of embodiments, system 800 can includefeature set producing module 802. In certain embodiments, feature setproducing module 802 can perform block 302 (FIG. 3) of producing afeature set.

In some embodiments, system 800 also can include a UGC analysis module804. In certain embodiments, UGC analysis module 804 can perform block304 (FIG. 3) of analyzing UGC to find mentions of each feature.

In various embodiments, system 800 further can include a total polaritydetermination module 806. In certain embodiments, total polaritydetermination module 806 can perform block 306 (FIG. 3) of determiningtotal polarity by analyzing mentions.

In various embodiments, system 800 additionally can include featurescore calculation module 808. In certain embodiments, feature scorecalculation module 808 can perform block 308 (FIG. 3) of presentingsearch results to the requester.

In various embodiments, order system 800 additionally can include finalfeature score calculation module 810. In certain embodiments, finalfeature score calculation module 810 can perform block 310 (FIG. 3) ofcalculating a final feature score.

In various embodiments, order system 800 additionally can includefeature score display module 812. In certain embodiments, feature scoredisplay module 812 can perform block 312 (FIG. 3) of displaying featurescores to a user.

In various embodiments, order system 800 additionally can include orderreceiving module. In certain embodiments, order receiving module canperform receiving an order from a user.

In various embodiments, order system 800 additionally can includedelivery facilitation module 816. In certain embodiments, deliveryfacilitation module 816 can perform block 316 (FIG. 3) of facilitatingthe delivery of the product to a user.

There can be several advantages to the systems and methods describedabove.

For example, another dimension is added to reviews. Annotating thereviews with relevant features of the product makes the reviews morereadable for users. Thus, information is more accessible to users as theuser can read only the reviews that discuss the features of interest tohim/her.

Another advantage is the ability to use vertical scoring. Instead ofthere being a single rating for the overall review where the singlerating might not accurately reflect what is important to some users, ananalysis of features talked about in the review can be obtained.Providing a feature score goes beyond product scoring. Features can behighlighted depending upon good or bad sentiment expressed and featurescores of different products can help in comparing the products usingonly a single rating.

Another advantage is that this method can be used for any textualcontent. This approach can be enhanced and tuned to work with othertextual contents to present sentiment expressed corresponding to variousissues related to the content.

Another advantage is the ability to monetize the data. For example, aneCommerce provider can determine the best and most popular feature of aproduct from the calculated feature scores and use those features as aselling point or a badge for the product to highlight it. The eCommerceprovider can sell information for specific products and/or generalproduct categories to product designers and manufacturers.

As disclosed above, there can be methods and systems to calculate afeature score for every feature of a product. The feature score can beused to annotate reviews for which the feature score is not equal tozero. If a feature has either a positive feature score or a negativefeature score, the implication is that the review mentions thatparticular feature. It is also possible for a review to have a neutralfeature score even if the feature is mentioned in the review. Forexample, a review can mention a certain feature twice, once with apositive connotation and once with a negative connotation. There are avariety of different ways in which such a situation can be handled. Insome embodiments, all feature scores that are zero can be ignored. Insome embodiments, all mentions of a feature can be displayed. In such amanner, if a particular review mentions a feature, that mention can beindicated even if the feature score for that feature is zero.

For a (feature, review) pair with a feature score that is less thanzero, the implication is that the feature has been mentioned negativelyin the review, so that particular (feature, review) pair can be markedas “Bad.”

For a (feature, review) pair with a feature score that is greater thanzero, the implication is that the feature has been mentioned positivelyin the review, so that particular (feature, review) pair can be markedas “Good.”

The scale used to determine if a pair is “Bad” or “Good” can rangebetween any two numbers. In some embodiments, the feature score canrange between −1 and 1. In other embodiments, the feature score canrange between 0 and 10. In other embodiments, the feature score canrange between 1 and 100. An embodiment can readily convert from onepolarity scale to a different polarity scale through the use ofaddition, subtraction, multiplication, division, and the like. Inaddition, the threshold score being used to determine if a score is“Bad” or “Good” can be changed depending a user's desires. In someembodiments, the threshold score can be changed by a user on a website.In some embodiments, the threshold score is not changeable by a typicaluser. For example, in some embodiments where the feature scores rangebetween −1 and 1, a score can be considered “good” if the score is over0, while a score can be considered “bad” if the score is 0 or below. Insome embodiments where the feature scores range between −1 and 1, ascore can be considered “good” if the score is over 0.2, while a scoreof −0.2 or less can be considered “bad,” with scores between −0.2 and0.2 not being considered “good” or “bad.” Other scores could also beused as a threshold score for determining if a review is “good,” “bad,”or “neutral.”

With reference to FIG. 9, a flowchart illustrating a method 900 forannotating reviews is presented. The review annotation can be performedon a computer system such as computer system 100 (FIG. 1). Method 900 ismerely exemplary and is not limited to the embodiments presented herein.Method 900 can be employed in many different embodiments or examples notspecifically depicted or described herein. In some embodiments, theprocedures, the processes and/or the activities of method 900 can beperformed in the order presented. In other embodiments, the procedures,the processes, and/or the activities of method 900 can be performed inany other suitable order. In still other embodiments, one or more of theprocedures, the processes, and/or the activities of method 900 can becombined or skipped.

In one embodiment, after feature scores are obtained (using, forexample, a method described above), one would tabulate each review andeach feature score in that review (block 902). There can be a set of nreviews: R₁, R₂, . . . , R_(n). There can be a feature set of mfeatures: f₁, f₂, . . . , f_(m). Each of the m features can have afeature score, established in a manner such as that set forth above.

The reviews and feature sets can be combined to form a two-dimensionalmatrix (block 904): {S₁₁, S₁₂, . . . , S_(1N); S₂₁, S₂₂, . . . , S_(2N);S_(M1), S_(M2), . . . , S_(MN)}.

Thereafter, the reviews are associated with features (block 906). Forexample, review R₁, has features f₁, f₂, and f₄ that have feature scoresthat are not equal to zero. Then R₁ gets associated with f₁, f₂, and f₄.In other words, each review is matched with the features that arediscussed in the review.

Thereafter, the features are labeled depending on the feature score(block 908). Features with a feature score greater than a giventhreshold score (e.g., zero), can get marked as “Good.” And the featureswith a feature score less than a given threshold score (e.g., zero), canget marked as “Bad.” Then the reviews can be displayed to the user witha tag indicating the feature and whether the feature score was “Good” or“Bad” (block 910). Displaying a review to the user can involvetransmitting data to a user's computer that cause the user's computer todisplay reviews.

There are several different advantages of such a method. For example,the above-presented annotation of reviews can help make the reviews morereadable to a user. In addition, an embodiment can lead to a display ofreviews that is more informative. A user is able to focus only onreviews that concern features of interest and ignore reviews thatconcern features with which he is not concerned.

There can also be a new layout/design to showcase customer reviews andinformation in reviews that can be difficult to find. Browsing customerreviews and other UGC can be as simple as viewing annotations of eachreview that note the attributes and features that each review discusses.Also, the sentiment of the author can be highlighted.

As discussed above, instead of displaying all the reviews as just largeamounts of text in various pages, an embodiment annotates reviews withthe features that each reviews discusses. In such a manner, a user canquickly determine what each review is discussing and what each review isabout without the need to read the entire review.

With reference to FIG. 10, an exemplary screen shot 1000 is presented.FIG. 10 is merely exemplary and embodiments of the screen representationare not limited to the embodiments presented herein. The screenrepresentation can be employed in many different embodiments or examplesnot specifically depicted or described herein.

Various aspects are present on screen 1000. Four reviews 1015, 1025,1035, and 1045 are present in screen 1000. On a large eCommerce site,the most popular products can have thousands of reviews. Only a portionof the reviews might be visible at a time. In the illustration of FIG.10, there happens to be four reviews. This is merely exemplary—there canbe many more than four reviews present on a single screen, or there canbe fewer than four reviews on a single screen.

The reviews in FIG. 10 are divided into two columns. Column 1010contains a description of each review. In some embodiments, thedescription is entered by a user. For example, a user submitting areview, will often submit a title of the review. As described above,sometimes the title of the review is not pertinent to actual content ofthe review, making the title of the review not as useful as it could be.For example, review 1025 is titled, “disappointed.” Such a title informsa user that the reviewer was not satisfied for some reason, but the useris left to guess at the reason. In another example, review 1045 istitled, “slow shipping.” Such a title tells the user nothing about theproduct except that the delivery was slower than expected.

Column 1020 contains a list of “tags” that represent features thatreceived either positive or negative feature scores in each review. Insome embodiments, the tags can be color-coded. In such a manner, onecolor can represent positive feature scores and another color canrepresent negative feature scores. Instead of color coding, someembodiments can use other methods of indicating the difference betweenpositive, negative, and neutral feature scores, such as shading, hatchmarks, underlining, typeface, font colors, and the like.

The result is that, with a quick glance, a user can determine whichreviews he wants to read more thoroughly—typically, those reviews thatdiscuss a feature that is important to the user. For example, if a useris interested in a particular camera's flash performance, he would beable to tell at a glance that only reviews 1015 and 1035 are pertinentto him. In some embodiments, the user will be able to click on the titleof the review to access the entirety of the review text, in a manner tobe detailed below.

In some embodiments, a user is able to click on a tag to view only thosereviews that contain features scores for that feature. Moving ahead toFIG. 11, a screen shot 1100 is presented. FIG. 11 is merely exemplaryand embodiments of the screen representation are not limited to theembodiments presented herein. The screen representation can be employedin many different embodiments or examples not specifically depicted ordescribed herein.

Screen 1100 illustrates a result of a user clicking on the “body” tagfrom screen 1000. Here, only reviews that have a non-zero feature scorefor the selected feature are presented to the user. It should beunderstood that it is also possible to present products with a featurescore of zero to the user.

There can be a title 1170. The title 1170 can serve to remind the useras to which product and feature the reviews pertain. Such a feature canbe handy because a user might have several browser windows or tabs openat once; if switching back and forth between reviews of differentproducts, it can be useful to be reminded what the product and featuresthe user is viewing. Reviews 1115, 1125, and 1135 are presented to theuser. On a large eCommerce site, the most popular products can havethousands of reviews. Only a portion of the reviews might be visible ata time. In the illustration of FIG. 11, there happens to be threereviews. It should be understood that there can be many more than threereviews present on a single screen. In a similar manner to thatdescribed above, the reviews can contain two columns, column 1110 andcolumn 1120. Column 1110 displays the title of the review. In someembodiments, only the tag that is the subject of the search is displayedto the user in column 1120. In some embodiments, all of the pertinenttags can be displayed to the user, to allow the user to view otherfeature scores. In some embodiments, instead of a tag being displayed incolumn 1120, because all of the reviews are relevant to a selectedfeature, an indication of whether the review is positive or negative canbe contained in column 1120.

In some embodiments, the user is able to select on a review (such as byclicking on the review title in column 1110) to view an excerpt of thereview, ranging up to the entire review. A user might wish to read anexcerpt or the entire review to judge how reliable the reviewer is. Insome embodiments, when the user selects a review to read, the text ofthe review, or an excerpt thereof, is displayed to the user. Inaddition, there can be elements of the review that can be highlighted,underlined, or otherwise distinguished from the remainder of the review.In the situation of FIG. 11, where each of the reviews are concerningthe “body” feature of the product, the sentences of the review thatcontain a reference to the “body” feature can be highlighted,underlined, or otherwise distinguished. The methods used to perform sucha feature are detailed below.

In addition to the list of reviews, there can also be a graphicalindicia that represents the distribution of positive and negativefeatures from the reviews. Pictured in FIG. 11 is a pie chart 1150. Piechart 1150 can contain various segments, such as positive segment 1160,neutral segment 1162, and negative segment 1164. Each of these segmentsis configured to illustrate the percentage of reviews that contain acertain feature score. In FIG. 11, the vast majority of reviews thatcontain a feature score for “body” are positive, so positive segment1160 is much larger than the other two segments. In some embodiments,there can be color-coding of the graphical indicia. For example,positive segment 1160 can be a first color, neutral segment 1162 can bea second color, and negative segment 1164 can be a third color. Thecolor-coding can extend to the tags in column 1120. For example, areview that contained a positive feature score for “body” can be thesame first color as positive segment 1160. In a similar manner, a reviewthat contained a neutral feature score for “body” can be the same secondcolor as neutral segment 1162 and a review that contained a negativefeature score for “body” can be the same third color as negative segment1164. While a pie chart is illustrated in FIG. 11, other embodiments canuse other types of graphical indicia, such as bar graphs, line graphs,histograms, and the like.

The creation of the graphical indicia can be accomplished in a varietyof different manners. In one embodiment, the number of reviews featuringa particular feature is known or can be determined through commondatabase operations. The creation of a graphical representation can beaccomplished by determining the total number of reviews with aparticular feature, the number of positive reviews of that particularfeature, the number of negative reviews of that particular feature, andthe number of neutral reviews of that particular features.

Other graphical indicia can be created. In one embodiment, the number ofreviews that discuss a particular feature can be compared to the totalnumber of reviews of the product. In such a manner, a user can moreeasily determine how important a feature is to the reviews.

The above layout can be beneficial in the sense that it will have a hugeimpact on customer satisfaction. Given the clutter that is currentlypresent over many customer review portals, an embodiment would provide asolution that would help in retaining old customers and attracting newcustomers. An embodiment can also lead to a customer spending more timeon a particular site, which can lead to increased revenue generation foran eCommerce provider using an embodiment.

Products can have many features that a prospective purchaser might haveto take into consideration before making a purchase. For example, adigital single lens reflex (DSLR) camera might have more than 50features to consider before buying it. A person shopping for a DSLRmight not know exactly what he needs and what features he shouldconcentrate on. Even after knowing the features you should look into, itcan still be desirable to read the reviews. The reviews can give aninsight as to how well the features work, where the product excels, andwhere the product needs improvement.

It can be desirable to provide, to a user, a list of the most importantfeatures of a product. After performing a feature-based sentimentanalysis on a set of features, as detailed above, the most importantfeatures of a product can be filtered out.

The filtering procedure can use heuristics to prioritize features basedon popularity and bias of the features. If a feature if highly regarded,then its popularity increases. If the sentiment of a sentence is highlypositive or highly negative, the sentence can be considered to be highlybiased.

In the following embodiment, it is assumed that the range of featurescores is from 0 to 1. In other embodiments, a different range of scorescan be used. In an embodiment where the range of feature scores is from0 to 1, the feature ranking can be calculated as follows:

With reference to FIG. 13, a flowchart illustrating a method 1300 forannotating reviews is presented. The review annotation can be performedon a computer system such as computer system 100 (FIG. 1). Method 1300is merely exemplary and is not limited to the embodiments presentedherein. Method 1300 can be employed in many different embodiments orexamples not specifically depicted or described herein. In someembodiments, the procedures, the processes and/or the activities ofmethod 1300 can be performed in the order presented. In otherembodiments, the procedures, the processes, and/or the activities ofmethod 1300 can be performed in any other suitable order. In still otherembodiments, one or more of the procedures, the processes, and/or theactivities of method 1300 can be combined or skipped.

The normalized score ranges between 0 and 1. Feature scores aregenerated for every feature of a product, possibly by using a methoddescribed earlier in this specification (block 1302). In general, thisprocess would involve analyzing each review, determining the featuresdescribed in each review, and assigning a feature score to each featurein each review. Once all the reviews are analyzed, all of the featureswith feature scores are determined (block 1304). The features can besorted in descending order of feature score (block 1306). Thereafter,the top K features can be selected (block 1308). K can be selected to beany number. As described above, some products can have more than 50features. It might not be desirable to list all the features of aproduct, as the list can be too long. Therefore, only a certain,predetermined number K can be chosen. In some embodiments, the top Kfeatures can be restricted by setting a minimum threshold score T (block1310). For example, because the normalized feature scores range from 0(bad) to 1 (good), a threshold score of 0.5 can be set. Thereafter, ifonly 5 features have a feature score of 0.5, only those 5 features aredisplayed to the user. It should be understood that the values of K andT can be fine-tuned depending on the desired results.

In some embodiments, after selecting the features, a visual indicia ofthe top features can be created (block 1312). In some embodiment, thevisual indicia can be a tag cloud. A tag cloud is a visualrepresentation of data, in this case, of features. The importance ofeach feature is shown by various features, such as font size and color.An exemplary tag cloud is shown in FIG. 14.

Moving ahead to FIG. 14, a screen shot 1400 is presented. FIG. 14 ismerely exemplary and embodiments of the screen representation are notlimited to the embodiments presented herein. The screen representationcan be employed in many different embodiments or examples notspecifically depicted or described herein.

Screen shot 1400 displays a portion of an exemplary display. The dataused to create screen shot 1400 can be transmitted to a user's computer,tablet, smartphone, or other similar device along with instructions tothe user's computer, tablet, smartphone, or other similar device thatcan be arranged to cause the device to display screen shot 1400.

Screen shot 1400 contains tag cloud 1450. There can be other elementspresent in screen shot 1400, but only tag cloud 1450 is shown forillustrative purposes. In this example, tag cloud 1450 includes each ofthe K features of an exemplary camera that have a feature score overthreshold T. As is typical in a tag cloud, tag cloud 1450 displays eachof the features with a different font size to differentiate between thefeatures. In this case, the features of tag cloud 1450 are distinguishedby the feature score of each feature—the higher the feature score, thelarger the font size of the feature.

Tag cloud 1450 contains 12 different features: face detection, pixel,battery, LCD (liquid crystal display), photo, screen, zoom, video, body,focus, lens, flash, and display. The features are sized and/or coloredto visually illustrate the score of the feature. For example, thefeature “zoom” has the largest font size, and therefore has the highestfeature score of any of the features. In contrast, the feature “lcd” hasthe smallest font size, and therefore has the lowest feature score overthreshold T.

In some embodiments, the size of the font can instead be used toillustrate the number of reviews with that feature and the color of thefont can be used to illustrate the feature score. For example, there canbe features with similar font sizes (for example, lens and focus in FIG.14). In such a case, the color of the words can be used to distinguishwhich feature has a higher feature score. A legend can be provided toaid a viewer in determining that, for example, the more green a fontcolor is, the more higher the feature score, while the more red a fontcolor is, the lower the feature score is.

With reference to FIG. 15, a flowchart illustrating a method 1500 forcreating a tag cloud based on reviews is presented. Method 1500 can beperformed on a computer system such as computer system 100 (FIG. 1).Method 1500 is merely exemplary and is not limited to the embodimentspresented herein. Method 1500 can be employed in many differentembodiments or examples not specifically depicted or described herein.In some embodiments, the procedures, the processes and/or the activitiesof method 1500 can be performed in the order presented. In otherembodiments, the procedures, the processes, and/or the activities ofmethod 1500 can be performed in any other suitable order. In still otherembodiments, one or more of the procedures, the processes, and/or theactivities of method 1500 can be combined or skipped.

The elements of method 1500 can be performed after block 1312 of FIG.13. However, the elements of method 1500 can also be performedindependently or as a part of a different method. With reference to FIG.15, the top K features and their feature scores are sorted by featurescore (block 1502). As stated above, the tag cloud can be based onvarious aspects of reviews. The embodiment illustrated in FIG. 15 willuse feature score. However, other embodiments, can use other aspects,such as number of reviews.

The top K features can be determined in one of a variety of differentmanners. Assuming the polarity scores range from 0 (for lowest rating)to 1 (for the highest rating), in one embodiment, the sum of thepolarity scores can be used to create an unnormalized score. In someembodiments, this value can be the sum of the maximum of the polarityand 1 minus the polarity, such that the lowest reviews of a feature arecaptured along with the highest reviews of a feature. In other words, auser might not be interested in just positive reviews of a certainfeature. The user also wants to know what people score a feature in anegative manner. The polarity ranges from 0 to 1. Using both thepolarity score and 1—the polarity score ensures that a feature score of0.8 is treated in the same manner as a feature score of 0.2.

Thereafter, the unnormalized scores can be normalized. In oneembodiment, each unnormalized score can be determined by taking theunnoramlized score, subtracting the smallest unnormalized score, anddividing the result by the highest unnormalized score.

After being sorted by normalized score, the largest font size F isdetermined (block 1504). In some embodiments, the largest font size isdependent on the medium by which the font will be displayed. Forexample, an embodiment being displayed on a mobile device might have adifferent largest font size than an embodiment being displayed on adesktop device.

Thereafter, using the number K of features to be tagged and, the fontsize of each of the features is determined (block 1506). In someembodiments, the font size can be distributed linearly. In someembodiments, the font size can be distributed logarithmically.

In an embodiment where the font size is distributed linearly, thefeature with the highest feature score is chosen to have the largestfont size F. The feature with the second highest feature score is chosento have the font size (K−1/K)*F. The feature with the third highestfeature score is chosen to have the font size (K−2/K)*F. And so on untilthe feature with the Kth lowest feature score has the font size (1/K)*F.In some embodiments, the font size is chosen by proportionally scalingthe “normalized feature score” between a range of font sizes between aminimum font size and a maximum font size. In some embodiments, theminimum font size is approximately 5 points and the maximum font size isapproximately 72 points. Other font sizes can be used in otherembodiments.

After the font sizes are chosen, the features are arranged in a randomorder (block 1508). Thereafter, the random arrangement of features indifferent font sizes is transmitted to a user for display (block 1510).

In some embodiments, the tag cloud can be configured to be clickable. Auser can click on one of the features listed in the tag cloud and beshown a list of reviews featuring that feature (such as the bottomportion of screen shot 1100 of FIG. 11). The tag cloud can be madeclickable in one of a variety of ways known in the art. For example, animage map can be created based on the random arrangement of features andfont sizes. Thereafter, the image map can be laid over the tag cloud insuch a manner that clicking on an area of the tag cloud results inclicking an area of the image map corresponding to the clicked area ofthe tag cloud. For example, with respect to FIG. 14, when a user clickson the area corresponding to “zoom,” the coordinates of the area beingclicked are s corresponding area of the image map. The image map has aset of coordinates that correspond to each feature.

In another aspect, an embodiment can help a user to understand a specialaspect of a set of reviews. Given an aspect and content in which a useris interested, the content can be annotated with sentiment andhighlights. If the user is interested in acquiring knowledge about aspecific aspect from multiple contents, a visualization of the contentcan be a great help to the user. The sentiment of the content also thesentiment related to the specific aspect.

Referring back to FIG. 11, pie chart 1150 shows the distribution ofdifferent sentiment in the reviews. Different reviews along with theirsentiment are listed. On clicking a specific review, an annotatedversion of the full review expands. The result is shown in FIG. 12.

In FIG. 12, we can see that the “Zoom” feature has positive, negative,and neutral reviews. These reviews can be highlighted in differentcolors to indicate the different categories. Such highlighting can helpthe user determine the sentences of the reviews that he can concentrateupon. It should be understood that the highlighting can use threedifferent colors, three different types of underlining, or any types oftechniques that can be used to distinguish between differences inreviews. In addition, other numbers of colors, underlining, orhighlighting can be used. In some embodiments, only two differentcolors, underlining, or highlighting can be used, with neutral reviewsbeing uncolored. In some embodiments, only one color, underlining, orhighlighting is used. In some embodiments, a user can select how manydifferent color, underlining, or highlighting is used and can selectwhich sentences (good, bad, or neutral) get colored, underlined, orhighlighted.

With reference to FIG. 16, a flowchart illustrating a method 1600 fordisplaying annotated reviews is presented. The review annotation can beperformed on a computer system such as computer system 100 (FIG. 1).Method 1600 is merely exemplary and is not limited to the embodimentspresented herein. Method 1600 can be employed in many differentembodiments or examples not specifically depicted or described herein.In some embodiments, the procedures, the processes and/or the activitiesof method 1600 can be performed in the order presented. In otherembodiments, the procedures, the processes, and/or the activities ofmethod 1600 can be performed in any other suitable order. In still otherembodiments, one or more of the procedures, the processes, and/or theactivities of method 1600 can be combined or skipped.

Each review is analyzed and a feature score is generated for everyfeature of a product, possibly using a method described earlier in thisspecification. Feature scores are generated for every feature of aproduct, possibly by using a method described earlier in thisspecification (block 1602). In general, this process would involveanalyzing each review, determining the features described in eachreview, and assigning a feature score to each feature in each review.Instead of merely assigning a feature score for each feature in eachreview, each portion of the review that mentions a feature is noted,such that every mention of every feature has a notation in a database(block 1604). For example, a review that mentions both the features“body” and “zoom” would be marked with both of those features. However,in addition, the sentence or sentences in each review that mention“body” or “zoom” would be noted.

Thereafter, an embodiment can receive input from a user indicating adesire to view details about a certain feature (block 1606). A graphicalindicia of the reviews can be displayed to the user, along with one ormore reviews, as illustrated above with respect to FIG. 11. As describedabove, a pie chart or other graphical indicia can be used to illustratethe distribution between positive reviews, negative reviews, and neutralreviews.

As shown in FIG. 11, a summary sentence is displayed to the user, alongwith an indication of whether the review was positive or negative. Theuser can then click on one of the reviews, resulting in the summaryshown in FIG. 12.

The entire review is now shown to the user as element 1240. Along withthe display of the review individual sentences of the review arehighlighted. For example, element 1242 shows a sentence discussing thefeature in a negative manner. Element 1244 shows a sentence discussingthe same feature in a neutral manner. Element 1246 shows a sentencediscussing the same feature in a positive manner.

Continuing to FIG. 16, the creation of such a display can be discussed.The database containing the reviews is consulted. The score of eachmention of each feature is accessed to determine if the mention ispositive, negative, or neutral. Thereafter, the mention is displayed ina different manner depending on the mention (block 1608).

In some database systems, the review portion would be stored in a singlefield. Thus, even a 1000-word (or longer) review would be contained in asingle field. In some embodiments, the review can be parsed, then storedin a plurality of fields in a database. For example, each sentence canbe stored in a separate field. Also stored in a separate field is thefeature that the sentence discusses. In such a manner, as each sentenceof a review is displayed, special formatting can be applied to eachsentence, depending on the content of the sentence. When the text of areview is to be displayed, each sentence can be retrieved individually.Then, the field indicating which feature is discussed by the sentence isaccessed to determine if the sentence should be emphasized in some way.If the user requested reviews regarding the feature “lens,” a sentencediscussing the “flash” will not receive emphasis. But a sentencediscussing “lens” will receive emphasis. As described above, theemphasis could be underlining, font color, highlighting, and the like.This process of retrieving a sentence and applying an emphasis can berepeated for each sentence in the review.

Although the above embodiments have been described with reference tospecific embodiments, it will be understood by those skilled in the artthat various changes might be made without departing from the spirit orscope of the disclosure. Accordingly, the disclosure of embodiments isintended to be illustrative of the scope of the disclosure and is notintended to be limiting. It is intended that the scope of the disclosureshall be limited only to the extent required by the appended claims. Forexample, to one of ordinary skill in the art, it will be readilyapparent that any element of FIGS. 1-16 can be modified, and that theforegoing discussion of certain of these embodiments does notnecessarily represent a complete description of all possibleembodiments. For example, one or more of the procedures, processes, oractivities of FIGS. 1-16 can include different procedures, processes,and/or activities and be performed by many different modules, in manydifferent orders.

All elements claimed in any particular claim are essential to theembodiment claimed in that particular claim. Consequently, replacementof one or more claimed elements constitutes reconstruction and notrepair. Additionally, benefits, other advantages, and solutions toproblems have been described with regard to specific embodiments. Thebenefits, advantages, solutions to problems, and any element or elementsthat can cause any benefit, advantage, or solution to occur or becomemore pronounced, however, are not to be construed as critical, required,or essential features or elements of any or all of the claims, unlesssuch benefits, advantages, solutions, or elements are stated in suchclaim.

Moreover, embodiments and limitations disclosed herein are not dedicatedto the public under the doctrine of dedication if the embodiments and/orlimitations: (1) are not expressly claimed in the claims; and (2) are orare potentially equivalents of express elements and/or limitations inthe claims under the doctrine of equivalents.

What is claimed is:
 1. A method being implemented via execution ofcomputing instructions configured to run at one or more processors andconfigured to be stored at non-transitory computer-readable media, themethod comprising: analyzing one or more reviews in a set of reviews ofa product on an ecommerce website to determine a set of features of theproduct analyzed in the set of reviews of the product; determining anormalized feature score for each feature in the set of features of theproduct by: using a sentiment analysis of the set of reviews of theproduct to determine an unnormalized feature score for each feature inthe set of features of the product by summing a polarity for eachmention of each feature in the set of features of the product; andcalculating the normalized feature score for each feature in the set offeatures of the product relative to other features in the set offeatures of the product based on the unnormalized feature score for eachfeature in the set of features; determining a plurality of top Kfeatures in the set of features of the product based on the normalizedfeature score for each feature in the set of features of the product;receiving a threshold score from an electronic device of a user;removing a first feature of the set of features of the product from theplurality of top K features in the set of features of the product when anormalized feature score of the first feature is less than the thresholdscore; determining a largest font size to be displayed on the electronicdevice of the user; calculating a font size for each feature of theplurality of top K features in the set of features of the product usingthe largest font size, the normalized feature score for each feature inthe set of features of the product, and a number K that corresponds to anumber of the plurality of top K features in the set of features of theproduct such that the font size of the first feature in the set offeatures of the product with a first normalized feature score is largerthan the font size of a second feature in the sct of features of theproduct with a second normalized feature score, wherein the secondnormalized feature score is less than the first normalized featurescore; randomizing a location for display on the electronic device ofthe user for each feature of the plurality of top K features in the setof features of the product on the ecommerce website; and transmittingfor display, on the electronic device of the user, a layout of theplurality of top K features in the set of features of the product on theecommerce website using the font size for each feature of the pluralityof top K features in the set of features of the product as calculatedand the location for display of each feature of the plurality of top Kfeatures in the set of features of the product.
 2. The method of claim 1further comprising: matching the set of features of the product with theset of reviews of the product to create an annotated set of reviews ofthe product, wherein each review of the annotated set of reviews of theproduct is matched with one or more annotations.
 3. The method of claim1 further comprising: creating an image map based on the layout of theplurality of top K features in the set of features of the product; andusing the image map to correspond each feature of the plurality of top Kfeatures in the set of features of the product with a hyperlink.
 4. Themethod of claim 3, wherein: the hyperlink for each feature of theplurality of top K features in the set of features of the product isconfigured to, when clicked, provide additional information regardingeach feature corresponding to the hyperlink.
 5. The method of claim 4,wherein: the additional information regarding each feature correspondingto the hyperlink comprises a listing of one or more reviews of theproduct that contain each feature associated with the hyperlink.
 6. Themethod of claim 1, wherein: calculating the normalized feature score foreach feature in the set of features of the product relative to the otherfeatures in the set of features of the product based on the unnormalizedfeature score for each feature comprises, for each unnormalized featurescore for each feature in the set of features of the product:determining a difference between (1) the unnormalized feature score fora feature in the set of features and (2) a smallest unnormalized featurescore in the set of features of the product by subtracting the smallestunnormalized feature score in the set of features of the product fromthe unnormalized feature score for the feature in the set of features ofthe product; and dividing the difference by a highest unnormalizedfeature score in the set of features of the product.
 7. The method ofclaim 6, wherein: determining the plurality of top K features in the setof features of the product comprises: sorting the set of features of theproduct by the normalized feature score for each feature in the set offeatures of the product; and selecting remaining features of the set offeatures of the product not removed in order of the normalized featurescore as sorted until K features of the set of features of the producthave been selected; and the method further comprises coordinatingdisplaying on the electronic device of the user of the normalizedfeature score of each of the remaining features of the set of featuresof the product not removed.
 8. The method of claim 1, whereindetermining the largest font size to be displayed on the electronicdevice of the user comprises: determining a number of pixels availableon a display of the electronic device of the user; and using apercentage of the number of pixels available to determine the largestfont size to be displayed.
 9. A system comprising: one or moreprocessing modules; and one or more non-transitory storage modulesstoring computing instructions configured to run on the one or moreprocessing modules and perform acts of: analyzing one or more reviews ina set of reviews of a product on an ecommerce website to determine a setof features of the product analyzed in the set of reviews of theproduct; determining a normalized feature score for each feature in theset of features of the product by: using a sentiment analysis of the setof reviews of the product to determine an unnormalized feature score foreach feature in the set of features of the product by summing a polarityfor each mention of each feature in the set of features of the product;and calculating the normalized feature score for each feature in the setof features of the product relative to other features in the set offeatures of the product based on the unnormalized feature score for eachfeature in the set of features, wherein the calculating comprises usinga following equation:${f_{i\; n} = \frac{f_{i} - f_{m\; i\; n}}{f_{m\; a\; x}}},$ whereinf_(in) is the normalized feature score for each feature in the set offeatures of the product, f_(i) is the unnormalized feature score foreach feature in the set of features of the product, f_(max) is a highestunnormalized feature score in the set of features of the product, andf_(min) is a lowest unnormalized feature score in the set of features ofthe product; determining a plurality of top K features in the set offeatures of the product based on the normalized feature score for eachfeature in the set of features of the product; determining a largestfont size to be displayed on an electronic device of a user; calculatinga font size for each feature of the plurality of top K features in theset of features of the product using the largest font size, thenormalized feature score for each feature in the set of features of theproduct, and a number K that corresponds to a number of the plurality oftop K features in the set of features of the product such that the fontsize of a first feature in the set of features of the product with afirst normalized feature score is larger than the font size of a secondfeature in the set of features of the product with a second normalizedfeature score, wherein the second normalized feature score is less thanthe first normalized feature score; randomizing a location for displayon the electronic device of the user for each feature of the pluralityof top K features in the set of features of the product on the ecommercewebsite; and transmitting for display a layout of the plurality of top Kfeatures in the set of features of the product on the ecommerce websiteusing the font size for each feature of the plurality of top K featuresin the set of features of the product as calculated and the location fordisplay of each feature of the plurality of top K features in the set offeatures of the product.
 10. The system of claim 9, wherein thecomputing instructions further comprise: matching the set of features ofthe product with the set of reviews of the product to create anannotated set of reviews of the product, wherein each review of theannotated set of reviews of the product is matched with one or moreannotations.
 11. The system of claim 9, wherein the computinginstructions further comprise: creating an image map based on the layoutof the plurality of top K features in the set of features of theproduct; and using the image map to correspond each feature of theplurality of top K features in the set of features of the product with ahyperlink.
 12. The system of claim 11, wherein: the hyperlink for eachfeature of the plurality of top K features in the set of features of theproduct is configured to, when clicked, provide additional informationregarding each feature corresponding to the hyperlink.
 13. The system ofclaim 12, wherein: the additional information regarding each featurecorresponding to the hyperlink comprises a listing of one or morereviews of the product that contain each feature associated with thehyperlink.
 14. The system of claim 9, wherein: determining the pluralityof top K features in the set of features of the product comprises:sorting the set of features of the product by the normalized featurescore for each feature in the set of features of the product; receivinga threshold score set by the user on the electronic device of the user;removing any feature of the set of features of the product from theplurality of top K features in the set of features of the product when anormalized feature score of any feature is less than the thresholdscore; and selecting remaining features of the set of features of theproduct not removed in order of the normalized feature score as sorteduntil K features of the set of features of the product have beenselected; and the computing instructions further comprise coordinatingdisplaying on the electronic device of the user of the normalizedfeature score of each of the remaining features of the set of featuresof the product not removed.
 15. The system of claim 9, whereindetermining the largest font size to be displayed on the electronicdevice of the user comprises: determining a number of pixels availableon a display unit of the electronic device of the user; and using apercentage of the number of pixels available to determine the largestfont size to be displayed.
 16. The method of claim 2, furthercomprising: coordinating displaying of the annotated set of reviews ofthe product, the annotated set of reviews of the product comprising: afirst column comprising a portion of each review of the annotated set ofreviews of the product describing a sentiment expressed by each reviewof the annotated set of reviews of the product, wherein the portion ofeach review of the annotated set of reviews of the product comprises ahyperlink configured to direct the user to a first additional webpagecomprising each review upon selection of the hyperlink by the user; anda second column comprising one or more tags for the portion of eachreview of the annotated set of reviews of the product, each of the oneor more tags representing a feature of the set of features of theproduct described by the sentiment in each review of the annotated setof reviews of the product, wherein each tag of the one or more tags fora description of each review of the annotated set of reviews of theproduct comprises a hyperlink configured to direct the user to a secondadditional webpage comprising only one or more reviews of the annotatedset of reviews of the product comprising feature scores for the each tagof the one or more tags.
 17. The method of claim 16, wherein the secondadditional webpage further comprises a graphical indicia representing adistribution of the normalized feature score associated with the eachtag of the one or more tags in the annotated set of reviews of theproduct.
 18. The system of claim 10, wherein the computing instructionsfurther comprise: coordinating displaying of the annotated set ofreviews of the product, the annotated set of reviews of the productcomprising: a first column comprising a portion of each review of theannotated set of reviews of the product describing a sentiment expressedby each review of the annotated set of reviews of the product, whereinthe description of each review of the annotated set of reviews of theproduct comprises a hyperlink configured to direct the user to a firstadditional webpage comprising the each review upon selection of thehyperlink by the user; and a second column comprising one or more tagsfor the portion of each review of the annotated set of reviews of theproduct, each of the one or more tags representing a feature of the setof features of the product described by the sentiment in each review ofthe annotated set of reviews of the product, wherein each tag of the oneor more tags for the description of each review of the annotated set ofreviews of the product comprises a hyperlink configured to direct theuser to a second additional webpage comprising only one or more reviewsof the annotated set of reviews of the product comprising feature scoresfor the each tag of the one or more tags.
 19. The system of claim 18,wherein the second additional webpage further comprises a graphicalindicia representing a distribution of the normal; led feature scoreassociated with the each tag of the one or more tags in the annotatedset of reviews of the product.
 20. The method of claim 1, wherein thepolarity for each mention of each feature in the set of features of theproduct is calculated using an equation:${{Total}\mspace{14mu}{{polarity}\left( {f_{i},R_{j}} \right)}} = {\sum\limits_{k = 1}^{n_{i\; j}}s_{k}}$wherein f_(i) is each feature in the set of features, R_(j) is eachreview in the set of reviews, n_(ij) is a number of occurrences of f_(i)in R_(j), and s_(k) is a polarity of each sentiment expressed in R_(j).21. The method of claim 1, wherein the unnormalized feature score foreach feature in the set of features of the product is calculated usingan equation:${{feature}\mspace{14mu}{{score}\left( {f_{i},R_{j}} \right)}} = \frac{\sum\limits_{k = 1}^{n_{ij}}s_{k}}{n_{i\; j}}$wherein f_(i) is each feature in the set of features, R_(j) is eachreview in the set of reviews, n_(ij) is a number of occurrences of f_(i)in R_(j), and s_(k) is a polarity of each sentiment expressed in R_(j).22. The system of claim 9, wherein the polarity for each mention of eachfeature in the set of features of the product is calculated using anequation:${{Total}\mspace{14mu}{{polarity}\left( {f_{i},R_{j}} \right)}} = {\sum\limits_{k = 1}^{n_{i\; j}}s_{k}}$wherein f_(i) is each feature in the set of features, R_(j) is eachreview in the set of reviews, n_(ij) is a number of occurrences of f_(i)in R_(j), and s_(k) is a polarity of each sentiment expressed in R_(j).23. The system of claim 9, wherein the unnormalized feature score foreach feature in the set of features of the product is calculated usingan equation:${{feature}\mspace{14mu}{{score}\left( {f_{i},R_{j}} \right)}} = \frac{\sum\limits_{k = 1}^{n_{ij}}s_{k}}{n_{i\; j}}$wherein f_(i) is each feature in the set of features, R_(j) is eachreview in the set of reviews, n_(ij) is a number of occurrences of f_(i)in R_(j), and s_(k) is a polarity of each sentiment expressed in R_(j).